



import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
    import transformers




def    _lowerCAmelCase  (	A__:      int						):


    '''simple docstring'''


    random.seed(_lowerCamelCase						)
    np.random.seed(_lowerCamelCase						)
    torch.manual_seed(_lowerCamelCase						)
    torch.cuda.manual_seed_all(_lowerCamelCase						)
    # ^^ safe to call this function even if cuda is not available


class    lowercase		:




       '''simple docstring'''







       def __init__(		self						,			_snake_case						,			_snake_case = 0.9999						,			_snake_case = 0.0						,			_snake_case = 0						,			_snake_case = False						,			_snake_case = 1.0						,			_snake_case = 2 / 3						,			_snake_case = None						,			_snake_case = None						,			**_snake_case						,			)   ->						Optional[Any]:

           """simple docstring"""


           if isinstance(SCREAMING_SNAKE_CASE_						,			torch.nn.Module  ):
               UpperCAmelCase      =       (
                   '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
                   '''Please pass the parameters of the module instead.'''
               )
               deprecate(
                   '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''						,			'''1.0.0'''						,			SCREAMING_SNAKE_CASE_						,			standard_warn=SCREAMING_SNAKE_CASE_						,			)
               UpperCAmelCase      =       parameters.parameters()

               # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
               UpperCAmelCase      =       True

           if kwargs.get('''max_value'''						,			SCREAMING_SNAKE_CASE_  ) is not None:
               UpperCAmelCase      =       '''The `max_value` argument is deprecated. Please use `decay` instead.'''
               deprecate('''max_value'''						,			'''1.0.0'''						,			SCREAMING_SNAKE_CASE_						,			standard_warn=SCREAMING_SNAKE_CASE_  )
               UpperCAmelCase      =       kwargs['''max_value''']

           if kwargs.get('''min_value'''						,			SCREAMING_SNAKE_CASE_  ) is not None:
               UpperCAmelCase      =       '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
               deprecate('''min_value'''						,			'''1.0.0'''						,			SCREAMING_SNAKE_CASE_						,			standard_warn=SCREAMING_SNAKE_CASE_  )
               UpperCAmelCase      =       kwargs['''min_value''']

           UpperCAmelCase      =       list(SCREAMING_SNAKE_CASE_  )
           UpperCAmelCase      =       [p.clone().detach() for p in parameters]

           if kwargs.get('''device'''						,			SCREAMING_SNAKE_CASE_  ) is not None:
               UpperCAmelCase      =       '''The `device` argument is deprecated. Please use `to` instead.'''
               deprecate('''device'''						,			'''1.0.0'''						,			SCREAMING_SNAKE_CASE_						,			standard_warn=SCREAMING_SNAKE_CASE_  )
               self.to(device=kwargs['''device''']  )

           UpperCAmelCase      =       None

           UpperCAmelCase      =       decay
           UpperCAmelCase      =       min_decay
           UpperCAmelCase      =       update_after_step
           UpperCAmelCase      =       use_ema_warmup
           UpperCAmelCase      =       inv_gamma
           UpperCAmelCase      =       power
           UpperCAmelCase      =       0
           UpperCAmelCase      =       None  # set in `step()`

           UpperCAmelCase      =       model_cls
           UpperCAmelCase      =       model_config






       @classmethod
       def 							snake_case_					(		cls						,			_snake_case						,			_snake_case  )   ->						"EMAModel":

           """simple docstring"""
           UpperCAmelCase  ,     UpperCAmelCase      =       model_cls.load_config(SCREAMING_SNAKE_CASE_						,			return_unused_kwargs=SCREAMING_SNAKE_CASE_  )
           UpperCAmelCase      =       model_cls.from_pretrained(SCREAMING_SNAKE_CASE_  )

           UpperCAmelCase      =       cls(model.parameters()						,			model_cls=SCREAMING_SNAKE_CASE_						,			model_config=model.config  )

           ema_model.load_state_dict(SCREAMING_SNAKE_CASE_  )
           return ema_model






       def 							snake_case_					(		self						,			_snake_case  )   ->						List[Any]:

           """simple docstring"""


           if self.model_cls is None:
               raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''  )

           if self.model_config is None:
               raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''  )

           UpperCAmelCase      =       self.model_cls.from_config(self.model_config  )
           UpperCAmelCase      =       self.state_dict()
           state_dict.pop('''shadow_params'''						,			SCREAMING_SNAKE_CASE_  )

           model.register_to_config(**SCREAMING_SNAKE_CASE_  )
           self.copy_to(model.parameters()  )
           model.save_pretrained(SCREAMING_SNAKE_CASE_  )






       def 							snake_case_					(		self						,			_snake_case  )   ->						float:

           """simple docstring"""
           UpperCAmelCase      =       max(0						,			optimization_step - self.update_after_step - 1  )

           if step <= 0:
               return 0.0

           if self.use_ema_warmup:
               UpperCAmelCase      =       1 - (1 + step / self.inv_gamma) ** -self.power
           else:
               UpperCAmelCase      =       (1 + step) / (10 + step)

           UpperCAmelCase      =       min(SCREAMING_SNAKE_CASE_						,			self.decay  )
           # make sure decay is not smaller than min_decay
           UpperCAmelCase      =       max(SCREAMING_SNAKE_CASE_						,			self.min_decay  )
           return cur_decay_value






       @torch.no_grad()
       def 							snake_case_					(		self						,			_snake_case  )   ->						Tuple:

           """simple docstring"""


           if isinstance(SCREAMING_SNAKE_CASE_						,			torch.nn.Module  ):
               UpperCAmelCase      =       (
                   '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
                   '''Please pass the parameters of the module instead.'''
               )
               deprecate(
                   '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''						,			'''1.0.0'''						,			SCREAMING_SNAKE_CASE_						,			standard_warn=SCREAMING_SNAKE_CASE_						,			)
               UpperCAmelCase      =       parameters.parameters()

           UpperCAmelCase      =       list(SCREAMING_SNAKE_CASE_  )

           self.optimization_step += 1

           # Compute the decay factor for the exponential moving average.
           UpperCAmelCase      =       self.get_decay(self.optimization_step  )
           UpperCAmelCase      =       decay
           UpperCAmelCase      =       1 - decay

           UpperCAmelCase      =       contextlib.nullcontext
           if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
               import deepspeed

           for s_param, param in zip(self.shadow_params						,			SCREAMING_SNAKE_CASE_  ):
               if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   UpperCAmelCase      =       deepspeed.zero.GatheredParameters(SCREAMING_SNAKE_CASE_						,			modifier_rank=SCREAMING_SNAKE_CASE_  )

               with context_manager():
                   if param.requires_grad:
                       s_param.sub_(one_minus_decay * (s_param - param)  )
                   else:
                       s_param.copy_(SCREAMING_SNAKE_CASE_  )






       def 							snake_case_					(		self						,			_snake_case  )   ->						None:

           """simple docstring"""
           UpperCAmelCase      =       list(SCREAMING_SNAKE_CASE_  )
           for s_param, param in zip(self.shadow_params						,			SCREAMING_SNAKE_CASE_  ):
               param.data.copy_(s_param.to(param.device  ).data  )






       def 							snake_case_					(		self						,			_snake_case=None						,			_snake_case=None  )   ->						None:

           """simple docstring"""
           UpperCAmelCase      =       [
               p.to(device=SCREAMING_SNAKE_CASE_						,			dtype=SCREAMING_SNAKE_CASE_  ) if p.is_floating_point() else p.to(device=SCREAMING_SNAKE_CASE_  )
               for p in self.shadow_params
           ]






       def 							snake_case_					(		self  )   ->						dict:

           """simple docstring"""
           return {
               "decay": self.decay,
               "min_decay": self.min_decay,
               "optimization_step": self.optimization_step,
               "update_after_step": self.update_after_step,
               "use_ema_warmup": self.use_ema_warmup,
               "inv_gamma": self.inv_gamma,
               "power": self.power,
               "shadow_params": self.shadow_params,
           }






       def 							snake_case_					(		self						,			_snake_case  )   ->						None:

           """simple docstring"""
           UpperCAmelCase      =       [param.detach().cpu().clone() for param in parameters]






       def 							snake_case_					(		self						,			_snake_case  )   ->						None:

           """simple docstring"""


           if self.temp_stored_params is None:
               raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''  )
           for c_param, param in zip(self.temp_stored_params						,			SCREAMING_SNAKE_CASE_  ):
               param.data.copy_(c_param.data  )

           # Better memory-wise.
           UpperCAmelCase      =       None






       def 							snake_case_					(		self						,			_snake_case  )   ->						None:

           """simple docstring"""
           UpperCAmelCase      =       copy.deepcopy(SCREAMING_SNAKE_CASE_  )

           UpperCAmelCase      =       state_dict.get('''decay'''						,			self.decay  )
           if self.decay < 0.0 or self.decay > 1.0:
               raise ValueError('''Decay must be between 0 and 1'''  )

           UpperCAmelCase      =       state_dict.get('''min_decay'''						,			self.min_decay  )
           if not isinstance(self.min_decay						,			SCREAMING_SNAKE_CASE_  ):
               raise ValueError('''Invalid min_decay'''  )

           UpperCAmelCase      =       state_dict.get('''optimization_step'''						,			self.optimization_step  )
           if not isinstance(self.optimization_step						,			SCREAMING_SNAKE_CASE_  ):
               raise ValueError('''Invalid optimization_step'''  )

           UpperCAmelCase      =       state_dict.get('''update_after_step'''						,			self.update_after_step  )
           if not isinstance(self.update_after_step						,			SCREAMING_SNAKE_CASE_  ):
               raise ValueError('''Invalid update_after_step'''  )

           UpperCAmelCase      =       state_dict.get('''use_ema_warmup'''						,			self.use_ema_warmup  )
           if not isinstance(self.use_ema_warmup						,			SCREAMING_SNAKE_CASE_  ):
               raise ValueError('''Invalid use_ema_warmup'''  )

           UpperCAmelCase      =       state_dict.get('''inv_gamma'''						,			self.inv_gamma  )
           if not isinstance(self.inv_gamma						,			(float, int)  ):
               raise ValueError('''Invalid inv_gamma'''  )

           UpperCAmelCase      =       state_dict.get('''power'''						,			self.power  )
           if not isinstance(self.power						,			(float, int)  ):
               raise ValueError('''Invalid power'''  )

           UpperCAmelCase      =       state_dict.get('''shadow_params'''						,			SCREAMING_SNAKE_CASE_  )
           if shadow_params is not None:
               UpperCAmelCase      =       shadow_params
               if not isinstance(self.shadow_params						,			SCREAMING_SNAKE_CASE_  ):
                   raise ValueError('''shadow_params must be a list'''  )
               if not all(isinstance(SCREAMING_SNAKE_CASE_						,			torch.Tensor  ) for p in self.shadow_params  ):
                   raise ValueError('''shadow_params must all be Tensors'''  )






"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}



class 			lowercase					( lowercase__		):
			lowercase        =     '''openai-gpt'''
			lowercase        =     {
			    '''max_position_embeddings''': '''n_positions''',
			    '''hidden_size''': '''n_embd''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}






			def __init__(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	str=40_478      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=512      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=768      ,SCREAMING_SNAKE_CASE_  :	Dict=12      ,SCREAMING_SNAKE_CASE_  :	Dict=12      ,SCREAMING_SNAKE_CASE_  :	int="gelu"      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	List[Any]=0.1      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=1e-5      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.02      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]="cls_index"      ,SCREAMING_SNAKE_CASE_  :	str=True      ,SCREAMING_SNAKE_CASE_  :	Any=None      ,SCREAMING_SNAKE_CASE_  :	List[Any]=True      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.1      ,**SCREAMING_SNAKE_CASE_  :	int      ,)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  vocab_size
				lowerCAmelCase												=  n_positions
				lowerCAmelCase												=  n_embd
				lowerCAmelCase												=  n_layer
				lowerCAmelCase												=  n_head
				lowerCAmelCase												=  afn
				lowerCAmelCase												=  resid_pdrop
				lowerCAmelCase												=  embd_pdrop
				lowerCAmelCase												=  attn_pdrop
				lowerCAmelCase												=  layer_norm_epsilon
				lowerCAmelCase												=  initializer_range
				lowerCAmelCase												=  summary_type
				lowerCAmelCase												=  summary_use_proj
				lowerCAmelCase												=  summary_activation
				lowerCAmelCase												=  summary_first_dropout
				lowerCAmelCase												=  summary_proj_to_labels
				super().__init__(**SCREAMING_SNAKE_CASE_			)


